政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/149466

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/149466

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题名:	統計機器學習：理論建構與因果分析 Statistical Machine Learning for Theory-building and Causal Analysis
作者:	周平 Chou, Ping
贡献者:	莊皓鈞周彥君 Chuang, Hao-Chun Chou, Yen-Chun 周平 Chou, Ping
关键词:	理論建構統計機器學習雙重機器學習統計與因果推論人機互動 Theory building statistical machine learning double machine learning statistical and causal inference human-system interaction
日期:	2023
上传时间:	2024-02-01 10:56:09 (UTC+8)
摘要:	解決管理問題並提供決策支援是管理研究的核心議題，其中，機器學習模型，尤其具有黑箱性質、被廣泛用於數學函數估計的演算法，在決策最佳化過程所需的預測及處方性分析扮演了關鍵的角色。儘管如此，在實證研究中，以機器學習方法幫助驗證理論有效或為真的效用並未吸引多數管理研究者的關注。隨著統計機器學習及通用性的模型解釋技術的進展，機器學習已成為統計及因果推論的有力工具。在探索變數相關性的描述性理論建構，少數頂尖管理期刊文章近來提倡將機器學習用於模式的探索和理論建構。然而，這些研究並未對變數及交互作用的重要性進行統計檢定，對於現有的統計機器學習技術亦缺乏系統性的整合。而在分析變數因果關係的規範性理論建構，計量經濟文獻雖然發展了基於機器學習的估計方法，但文獻仍缺乏相關技術在資訊及作業管理的實證應用。為彌補上述研究缺口，本學位論文包含了兩篇研究。第一篇研究闡述了統計機器學習如何基於相關性進行解釋及驗證性的理論建構。當中，本研究提出了一套以隨機森林為基礎的研究流程，能提供演繹及溯因推理性的理論建構，針對變數與交互作用的重要性進行統計檢定，從而偵測資料中隱藏的模式並測試變數的相關性。透過廣泛的模擬實驗，本研究指出機器學習有較卓越表現的情境，以及與傳統統計分析方法互補的情境。第二篇研究介紹了一套基於機器學習的通用因果推論技術──雙重機器學習；本研究除透過模擬實驗來驗證該技術在因果推論的有效性及穩健性，同時將其應用於零售供應鏈情境下的庫存管理實證分析，探討半自動化的決策支援系統建議與經理人修正的人機互動模式。本研究從動態面板數據中發掘了數個穩健的因果模式，這些模式指出庫存管理績效可能因為經理人修改系統決策而得到改善的情境。綜上所述，本學位論文的兩篇研究皆致力於整合、驗證，及應用前沿的機器學習於理論建構，從而為方法論文獻（機器學習的統計推論）及實證作業管理和人機互動研究提出貢獻。 Problem-solving and decision support represent a prominent discipline in the management literature, whereas Machine Learning (ML) – the black-box algorithms dedicated to function-fitting in particular – serves as pivotal tools for predictive and prescriptive analytics for decision optimization. However, management researchers tend to overlook ML’s potential as a research methodology for empirical research, which answers the central inquiry of theory building “Is it valid or true.” The up-to-date developments in statistical ML and model-agnostic interpretation have rendered the algorithms decent statistical and causal inference tools. For descriptive theory development reliant on association, recent works from authoritative management journals have begun using ML for pattern discovery and theory building. Nevertheless, they fall short of offering statistical tests for variable/interaction importance and integrating them with the extant techniques. For causality that facilitates normative theory, the econometrics literature has devised ML-based causal effect estimators but lacks empirical application in information and operations management research. To fill in the gaps, this dissertation comprises two essays. The first essay articulates the utilization of statistical ML for explanatory and confirmatory theory-building with associations. We propose an analysis protocol based on Random Forest (RF) for abductive and deductive theory building, equipped with statistical tests for the significance of variables and the interactions. The protocol helps uncover the patterns and test the association. Based on extensive simulations, we shed light on the context where MLs exhibit superiority and are complementary to the prevalently used econometrics model for empirical data analysis. For the second essay, we introduce Double Machine Learning (DML), a general framework of ML-based causal inference, for theory building. In addition to numerically assessing the statistical properties/robustness of DML, we use DML to empirically analyze human-system interaction in the context of semi-automated replenishment decision-making. Reliant on the proven statistical validity of DML for dynamic panel data, we uncover robust causal patterns for the contexts where managers’ interventions to the algorithmic decisions improve inventory performance. The two essays are dedicated to integrating, validating, and employing sophisticated ML techniques for theory building, and make contributions to the methodological literature on ML-based inference in management and the literature on empirical operations management and human-system interaction in decision-making.
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